{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "482449e5-c5c7-42f4-ac86-a1ebd5868b40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liuanqi/opt/anaconda3/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from textblob import TextBlob\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib.font_manager import FontProperties"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0d0804-4cb3-435b-a9ff-ff48c6d498c8",
   "metadata": {},
   "source": [
    "# Alabama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6b288b81-a24f-4c92-af8b-7a04909ac25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data= pd.read_csv('./AL_complaint_data_250210.csv')\n",
    "data['datetime'] = pd.to_datetime(data['datetime'])\n",
    "data = data.reset_index(drop=True)\n",
    "df = data.drop(['Unnamed: 0.1','Unnamed: 0'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "38bc6382-91ff-4f77-8507-64fcbe711c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "counties = pd.DataFrame(df.groupby(df.County).count().index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "16cda667-18d1-4546-af78-eebfd09b1a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>County</th>\n",
       "      <th>GeoFips</th>\n",
       "      <th>GeoName</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AUTAUGA</td>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>1.808698e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BALDWIN</td>\n",
       "      <td>1003</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>7.808667e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BARBOUR</td>\n",
       "      <td>1005</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>7.998268e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>BIBB</td>\n",
       "      <td>1007</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>4.414082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>BLOUNT</td>\n",
       "      <td>1009</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>1.010542e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>TUSCALOOSA</td>\n",
       "      <td>1125</td>\n",
       "      <td>Tuscaloosa, AL</td>\n",
       "      <td>1.055627e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>WALKER</td>\n",
       "      <td>1127</td>\n",
       "      <td>Walker, AL</td>\n",
       "      <td>1.951076e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>1129</td>\n",
       "      <td>Washington, AL</td>\n",
       "      <td>8.979380e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>WILCOX</td>\n",
       "      <td>1131</td>\n",
       "      <td>Wilcox, AL</td>\n",
       "      <td>4.034955e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>WINSTON</td>\n",
       "      <td>1133</td>\n",
       "      <td>Winston, AL</td>\n",
       "      <td>8.006684e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0      County  GeoFips         GeoName          mean\n",
       "0            0     AUTAUGA     1001     Autauga, AL  1.808698e+06\n",
       "1            1     BALDWIN     1003     Baldwin, AL  7.808667e+06\n",
       "2            2     BARBOUR     1005     Barbour, AL  7.998268e+05\n",
       "3            3        BIBB     1007        Bibb, AL  4.414082e+05\n",
       "4            4      BLOUNT     1009      Blount, AL  1.010542e+06\n",
       "..         ...         ...      ...             ...           ...\n",
       "62          62  TUSCALOOSA     1125  Tuscaloosa, AL  1.055627e+07\n",
       "63          63      WALKER     1127      Walker, AL  1.951076e+06\n",
       "64          64  WASHINGTON     1129  Washington, AL  8.979380e+05\n",
       "65          65      WILCOX     1131      Wilcox, AL  4.034955e+05\n",
       "66          66     WINSTON     1133     Winston, AL  8.006684e+05\n",
       "\n",
       "[67 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = pd.read_csv('./AL_gdp_average_250210.csv')\n",
    "\n",
    "gdp = gdp.sort_values('GeoName')\n",
    "gdp = gdp.reset_index(drop=True)\n",
    "gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9ff4fa51-aeaf-4c19-81f9-2004f1d9133c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['JEFFERSON', 'MADISON', 'MOBILE', 'MONTGOMERY', 'SHELBY',\n",
       "       'TUSCALOOSA', 'BALDWIN', 'LEE', 'MORGAN', 'HOUSTON', 'CALHOUN',\n",
       "       'LIMESTONE', 'MARSHALL', 'TALLADEGA', 'ETOWAH'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top15_list = gdp.sort_values('mean',ascending=False)[:15].County.values\n",
    "top15_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "64375f4a-c4ed-47a7-a88f-679f358a7406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime  monthly\n",
       "0 2012-01-01       93\n",
       "1 2012-02-01      111\n",
       "2 2012-03-01      123\n",
       "3 2012-04-01       89\n",
       "4 2012-05-01      114"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly = df['datetime'].groupby(df.datetime.dt.to_period(\"M\")).agg('count')\n",
    "monthly = pd.DataFrame(monthly)\n",
    "monthly.columns= (['monthly'])\n",
    "monthly = monthly.reset_index(drop=False)\n",
    "monthly['datetime'] = monthly['datetime'].dt.to_timestamp('s').dt.strftime('%Y-%m-%d')\n",
    "monthly['datetime'] = pd.to_datetime(monthly['datetime'])\n",
    "monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a85857fa-6b74-4b5b-b768-e20d4321acbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthly</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>46</td>\n",
       "      <td>0.494624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>65</td>\n",
       "      <td>0.585586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>58</td>\n",
       "      <td>0.471545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>43</td>\n",
       "      <td>0.483146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>74</td>\n",
       "      <td>0.649123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime  monthly  percentage\n",
       "0 2012-01-01       46    0.494624\n",
       "1 2012-02-01       65    0.585586\n",
       "2 2012-03-01       58    0.471545\n",
       "3 2012-04-01       43    0.483146\n",
       "4 2012-05-01       74    0.649123"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top15_list = ['JEFFERSON','MOBILE','MADISON','MONTGOMERY','SHELBY',\n",
    "#               'TUSCALOOSA','BALDWIN','LEE','MORGAN','HOUSTON','CALHOUN','LIMESTONE','MARSHALL','TALLADEGA','CULLMAN']\n",
    "\n",
    "top15 = []\n",
    "top15 = pd.DataFrame(top15)\n",
    "for name in top15_list:\n",
    "    res = df[df['County'].str.contains(name)]\n",
    "    top15 = pd.concat([top15,res],axis=0)\n",
    "\n",
    "top15_monthly = top15['datetime'].groupby(top15.datetime.dt.to_period(\"M\")).agg('count')\n",
    "top15_monthly = pd.DataFrame(top15_monthly)\n",
    "top15_monthly.columns= (['monthly'])\n",
    "top15_monthly = top15_monthly.reset_index(drop=False)\n",
    "top15_monthly['datetime'] = top15_monthly['datetime'].dt.to_timestamp('s').dt.strftime('%Y-%m-%d')\n",
    "top15_monthly['datetime'] = pd.to_datetime(top15_monthly['datetime'])\n",
    "top15_monthly['percentage'] = top15_monthly['monthly']/monthly['monthly']\n",
    "top15_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cbf42380-4337-4e46-a6a6-0768a00b3719",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "# Path to your TTF file\n",
    "ttf_path = '/Users/liuanqi/Library/Fonts (Removed)/06-17-2024 19_25_42 GMT+9/Pretendard-Regular.ttf'\n",
    "\n",
    "# Load the TTF file\n",
    "custom_font = FontProperties(fname=ttf_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "00dbd8e3-6c60-4a31-9d8c-ca4d34ac5bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "ticks = [datetime(2007,1,1),datetime(2008,1,1),datetime(2009,1,1),datetime(2010,1,1),datetime(2011,1,1),datetime(2012,1,1)\n",
    "         ,datetime(2013,1,1),datetime(2014,1,1),datetime(2015,1,1),datetime(2016,1,1),datetime(2017,1,1),datetime(2018,1,1),datetime(2019,1,1),datetime(2020,1,1)\n",
    "        ,datetime(2021,1,1),datetime(2022,1,1),datetime(2023,1,1),datetime(2024,1,1)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a0b903e-8b91-4d20-bd98-8ec31ec3649c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "with sns.axes_style(\"white\"):\n",
    "    sns.set_style(\"ticks\")         # import sns使得横纵坐标的线加浓加粗\n",
    "    sns.set_context(\"talk\")\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "import datetime\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "line1,  = plt.step(monthly['datetime'], monthly['monthly'],color='black',ms=9,label='Total complaints')\n",
    "line2,  = plt.step(top15_monthly['datetime'], top15_monthly['monthly'],color='r',ms=6,label='Top 15 counties')\n",
    "\n",
    "plt.ylabel('Monthly complaints',fontproperties=custom_font,fontsize=25)\n",
    "plt.xticks(ticks, fontsize=25, rotation=0)\n",
    "ax.set_xticklabels(['2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020','2021','2022','2023','2024'],fontproperties=custom_font,fontsize=25)\n",
    "\n",
    "plt.grid(color='grey', linestyle=':', linewidth=0.9, which='major',axis='y')\n",
    "ax.set_xlim([datetime.date(2010, 11, 1), datetime.date(2024, 2, 1)])\n",
    "plt.yticks(np.arange(0,250,50),fontsize=28,color='black')\n",
    "#line_legend = ax.legend(handles=[line1, line2], fontsize=20, loc='upper left', edgecolor='black', title=\"\", ncol=2,columnspacing=1.2)\n",
    "\n",
    "\n",
    "#plt.axvline(x=[datetime.datetime(2020, 1, 1)], ymin=0.0, ymax=80, color='black',ls='--', linewidth = 3)\n",
    "#plt.axvline(x=[datetime.datetime(2022, 1, 1)], ymin=0.0, ymax=80, color='black',ls='--',linewidth = 3)\n",
    "#plt.axvline(x=[datetime.datetime(2018, 8, 1)], ymin=0.0, ymax=80, color='black',ls='--',linewidth = 3)\n",
    "flood = plt.axvspan(datetime.datetime(2018, 8, 1),datetime.datetime(2019, 4, 1), ymin=0.0, ymax=80, color='lightblue',ls='-',alpha=0.3,linewidth = 3,label='Flood')\n",
    "drought = plt.axvspan(datetime.datetime(2016, 5, 1),datetime.datetime(2017, 5, 1), ymin=0.0, ymax=80, color='r',ls='-',alpha=0.1,linewidth = 1,label='Drought')\n",
    "covid = plt.axvspan(datetime.datetime(2020, 1, 1),datetime.datetime(2022, 1, 1), ymin=0.0, ymax=80, color='lightgrey',ls='-',alpha=0.4,linewidth = 3,label='COVID-19')\n",
    "ax = plt.gca()\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(3)\n",
    "\n",
    "#highlight_legend = ax.legend(handles=[flood, drought, covid], fontsize=20, loc=(0.01,0.74), ncol=3,edgecolor='black', title=\"\",columnspacing=1.35)\n",
    "#ax.add_artist(line_legend)\n",
    "#plt.legend(fontsize=20,loc='upper left',edgecolor='black', ncol=2)\n",
    "#plt.savefig('./AL_monthly_complaints_new.png', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a15d88ca-1d4a-4de7-94c5-2c812f59843d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getTextSubjectivity(txt):\n",
    "    return TextBlob(txt).sentiment.subjectivity\n",
    "\n",
    "def getTextPolarity(txt):\n",
    "    return TextBlob(txt).sentiment.polarity\n",
    "\n",
    "\n",
    "\n",
    "df['Subjectivity'] = df['description'].apply(getTextSubjectivity)  \n",
    "df['Polarity'] = df['description'].apply(getTextPolarity)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6fa768-8f91-4307-89a9-6886d57a3163",
   "metadata": {},
   "source": [
    "# remove subjectivity ==0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "84195cda-a51a-497a-ac1e-c62c8be07f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>County</th>\n",
       "      <th>complaint_id</th>\n",
       "      <th>datetime</th>\n",
       "      <th>received_via</th>\n",
       "      <th>received_by</th>\n",
       "      <th>description</th>\n",
       "      <th>Subjectivity</th>\n",
       "      <th>Polarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JEFFERSON</td>\n",
       "      <td>2E-003MU3N44</td>\n",
       "      <td>2012-01-03 01:53:00</td>\n",
       "      <td>Phone</td>\n",
       "      <td>Rogers, Paul</td>\n",
       "      <td>Sewage odor that is not constant but is presen...</td>\n",
       "      <td>0.472222</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BUTLER</td>\n",
       "      <td>0K-004DL0P01</td>\n",
       "      <td>2012-01-03 02:01:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>Deer processing plant not disposing of remaini...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ST. CLAIR</td>\n",
       "      <td>0G-008GP0C67</td>\n",
       "      <td>2012-01-03 08:06:00</td>\n",
       "      <td>Phone</td>\n",
       "      <td>Holcombe, Gayle</td>\n",
       "      <td>Clearing and grading taking place near the cre...</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ST. CLAIR</td>\n",
       "      <td>5P-000CH1T15</td>\n",
       "      <td>2012-01-03 08:14:00</td>\n",
       "      <td>Phone</td>\n",
       "      <td>Holcombe, Gayle</td>\n",
       "      <td>Large equipment being used in and around canoe...</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>-0.192857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ST. CLAIR</td>\n",
       "      <td>4U-000DT2I67</td>\n",
       "      <td>2012-01-03 10:51:00</td>\n",
       "      <td>Phone</td>\n",
       "      <td>Holcombe, Gayle</td>\n",
       "      <td>pushing dirt down by the creek behind the foot...</td>\n",
       "      <td>0.494444</td>\n",
       "      <td>-0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14794</th>\n",
       "      <td>MOBILE</td>\n",
       "      <td>0T-000JX1P26</td>\n",
       "      <td>2023-12-29 04:58:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>There appears to have been a low pressure forc...</td>\n",
       "      <td>0.321667</td>\n",
       "      <td>-0.026667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14795</th>\n",
       "      <td>ETOWAH</td>\n",
       "      <td>5N-002DF4G17</td>\n",
       "      <td>2023-12-29 06:47:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>More than an acre of land has been disturbed w...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14796</th>\n",
       "      <td>PERRY</td>\n",
       "      <td>3D-007WY1A70</td>\n",
       "      <td>2023-12-31 06:45:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>Our water in Marion Alabama is brown and smell...</td>\n",
       "      <td>0.178571</td>\n",
       "      <td>-0.040476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14797</th>\n",
       "      <td>MADISON</td>\n",
       "      <td>6Q-007XC8L33</td>\n",
       "      <td>2023-12-31 06:58:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>A local farmer/contractor has destroyed approx...</td>\n",
       "      <td>0.449167</td>\n",
       "      <td>0.000833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14798</th>\n",
       "      <td>PERRY</td>\n",
       "      <td>8P-003MM1Y56</td>\n",
       "      <td>2023-12-31 07:50:00</td>\n",
       "      <td>Web</td>\n",
       "      <td>Web Complaint</td>\n",
       "      <td>Absolutely no water from 12/28/23 10:30 pm unt...</td>\n",
       "      <td>0.380000</td>\n",
       "      <td>-0.045000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12682 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          County  complaint_id            datetime received_via  \\\n",
       "1      JEFFERSON  2E-003MU3N44 2012-01-03 01:53:00        Phone   \n",
       "2         BUTLER  0K-004DL0P01 2012-01-03 02:01:00          Web   \n",
       "4      ST. CLAIR  0G-008GP0C67 2012-01-03 08:06:00        Phone   \n",
       "5      ST. CLAIR  5P-000CH1T15 2012-01-03 08:14:00        Phone   \n",
       "6      ST. CLAIR  4U-000DT2I67 2012-01-03 10:51:00        Phone   \n",
       "...          ...           ...                 ...          ...   \n",
       "14794     MOBILE  0T-000JX1P26 2023-12-29 04:58:00          Web   \n",
       "14795     ETOWAH  5N-002DF4G17 2023-12-29 06:47:00          Web   \n",
       "14796      PERRY  3D-007WY1A70 2023-12-31 06:45:00          Web   \n",
       "14797    MADISON  6Q-007XC8L33 2023-12-31 06:58:00          Web   \n",
       "14798      PERRY  8P-003MM1Y56 2023-12-31 07:50:00          Web   \n",
       "\n",
       "           received_by                                        description  \\\n",
       "1         Rogers, Paul  Sewage odor that is not constant but is presen...   \n",
       "2        Web Complaint  Deer processing plant not disposing of remaini...   \n",
       "4      Holcombe, Gayle  Clearing and grading taking place near the cre...   \n",
       "5      Holcombe, Gayle  Large equipment being used in and around canoe...   \n",
       "6      Holcombe, Gayle  pushing dirt down by the creek behind the foot...   \n",
       "...                ...                                                ...   \n",
       "14794    Web Complaint  There appears to have been a low pressure forc...   \n",
       "14795    Web Complaint  More than an acre of land has been disturbed w...   \n",
       "14796    Web Complaint  Our water in Marion Alabama is brown and smell...   \n",
       "14797    Web Complaint  A local farmer/contractor has destroyed approx...   \n",
       "14798    Web Complaint  Absolutely no water from 12/28/23 10:30 pm unt...   \n",
       "\n",
       "       Subjectivity  Polarity  \n",
       "1          0.472222  0.083333  \n",
       "2          1.000000 -1.000000  \n",
       "4          0.400000  0.100000  \n",
       "5          0.614286 -0.192857  \n",
       "6          0.494444 -0.277778  \n",
       "...             ...       ...  \n",
       "14794      0.321667 -0.026667  \n",
       "14795      0.500000  0.500000  \n",
       "14796      0.178571 -0.040476  \n",
       "14797      0.449167  0.000833  \n",
       "14798      0.380000 -0.045000  \n",
       "\n",
       "[12682 rows x 8 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remove = df[df['Subjectivity'] == 0]\n",
    "df2 = pd.concat([df, remove]).drop_duplicates(keep=False)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d386415-48f2-463e-ae25-78682e5edf60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制结果\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "bins = np.arange(0,1.1,0.05)\n",
    "\n",
    "plt.hist(df.Subjectivity, bins=bins, color='#a6cee3',edgecolor='black',alpha=0.8)\n",
    "\n",
    "ax.set_xticks(np.arange(0,1.1,0.1))\n",
    "xticks = ax.get_xticks()\n",
    "ax.set_xticklabels([f'{x:.1f}' for x in xticks], fontproperties=custom_font, fontsize=25)\n",
    "\n",
    "ax.set_xlabel('Subjectivity ',  fontproperties=custom_font, fontsize=30,)\n",
    "ax.set_ylabel('Frequency',fontproperties=custom_font, fontsize=30, )\n",
    "legend_properties = FontProperties(fname=ttf_path, size=20)\n",
    "#ax.legend(prop=legend_properties, ncol=2)\n",
    "#ax.set_yticklabels(['0.00','0.02','0.04','0.06','0.08','0.10','0.12','0.14','0.16'],fontproperties=custom_font)\n",
    "#ax.tick_params(axis='both', which='major', labelsize=25)\n",
    "# 设置x轴和y轴标签的小数点精确到两位，并应用自定义字体\n",
    "#plt.ylim(0,1400)\n",
    "xticks = ax.get_xticks()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_xticklabels([f'{x:.1f}' for x in xticks], fontproperties=custom_font, fontsize=25)\n",
    "ax.set_yticklabels([f'{int(y)}' for y in yticks], fontproperties=custom_font, fontsize=25)\n",
    "plt.xlim(0,1)\n",
    "plt.axvspan(xmin=0,xmax=0.3,ymin=0,ymax=1400,alpha=0.1)\n",
    "plt.axvspan(xmin=0.4,xmax=1,ymin=0,ymax=1400,alpha=0.08,color='r')\n",
    "ax.xaxis.set_tick_params(pad=15) \n",
    "ax.yaxis.set_tick_params(pad=2) \n",
    "plt.axvline(x=0.3,color='b',ls='--',lw=2)\n",
    "plt.axvline(x=0.4,color='r',ls='--',lw=2)\n",
    "\n",
    "percentage_obj = round((len(df[df['Subjectivity'] <=0.3].Subjectivity)/ len(df.Subjectivity)*100),2)\n",
    "percentage_sub = round((len(df[df['Subjectivity'] >=0.4].Subjectivity)/ len(df.Subjectivity)*100),2)\n",
    "plt.text(0.07,1800,str(percentage_obj)+'%', fontsize=30,color='b')\n",
    "plt.text(0.62,1800,str(percentage_sub)+'%', fontsize=30,color='r')\n",
    "ax = plt.gca()  # 获取当前子图的轴对象\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(2.5)  # 设置边框线宽\n",
    "#plt.savefig('./AL_subjectivity_before_remove.png', dpi=300,bbox_inches='tight')\n",
    "#ax.set_title('Sensitivity Analysis of Emotion Scores by Subjectivity Threshold', fontsize=40, fontproperties=custom_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3c84c3b4-a86d-442c-9767-ca104b6a404b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制结果\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "#bins = np.linspace(0, 1, 20) \n",
    "plt.hist(df2.Subjectivity, bins=bins, color='#a6cee3',edgecolor='black',alpha=0.8)\n",
    "ax.set_xticks(np.arange(0,1.1,0.2))\n",
    "\n",
    "legend_properties = FontProperties(fname=ttf_path, size=20)\n",
    "ax.set_xlabel('Subjectivity ',  fontproperties=custom_font, fontsize=30,)\n",
    "ax.set_ylabel('Frequency',fontproperties=custom_font, fontsize=30, )\n",
    "ax.set_xticks(np.arange(0,1.1,0.1))\n",
    "xticks = ax.get_xticks()\n",
    "ax.set_xticklabels([f'{x:.1f}' for x in xticks], fontproperties=custom_font, fontsize=25)\n",
    "\n",
    "# yticks = ax.get_yticks()\n",
    "# ax.set_yticklabels([f'{int(y)}' for y in yticks], fontproperties=custom_font, fontsize=25)\n",
    "ax.set_yticks(np.arange(0,2000,200))\n",
    "ax.set_yticklabels(np.arange(0,2000,200), fontproperties=custom_font, fontsize=25)\n",
    "plt.xlim(0,1)\n",
    "plt.axvspan(xmin=0,xmax=0.3,ymin=0,ymax=1400,alpha=0.1)\n",
    "plt.axvspan(xmin=0.4,xmax=1,ymin=0,ymax=1400,alpha=0.08,color='r')\n",
    "ax.xaxis.set_tick_params(pad=15) \n",
    "\n",
    "ax.yaxis.set_tick_params(pad=2) \n",
    "plt.axvline(x=0.3,color='b',ls='--',lw=2)\n",
    "plt.axvline(x=0.4,color='r',ls='--',lw=2)\n",
    "#plt.ylim(0,800)\n",
    "percentage_obj = round((len(df2[df2['Subjectivity'] <=0.3].Subjectivity)/ len(df2.Subjectivity)*100),2)\n",
    "percentage_sub = round((len(df2[df2['Subjectivity'] >=0.4].Subjectivity)/ len(df2.Subjectivity)*100),2)\n",
    "plt.text(0.02,1500,str(percentage_obj)+'%', fontsize=30,color='b')\n",
    "plt.text(0.62,1500,str(percentage_sub)+'%', fontsize=30,color='r')\n",
    "\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(2.5)  # 设置边框线宽\n",
    "    \n",
    "#plt.savefig('./AL_subjectivity_after_remove.png', dpi=300,bbox_inches='tight')\n",
    "\n",
    "#ax.set_title('Sensitivity Analysis of Emotion Scores by Subjectivity Threshold', fontsize=40, fontproperties=custom_font)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ab69c2-f73b-4d35-9129-611aa287cf27",
   "metadata": {},
   "source": [
    "# Arkansas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "281ba577-38e9-44c4-9eb4-6ab690127edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-03-01</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime  monthly\n",
       "0   2011-01-01       44\n",
       "1   2011-02-01       51\n",
       "2   2011-03-01       58\n",
       "3   2011-04-01       64\n",
       "4   2011-05-01       60\n",
       "..         ...      ...\n",
       "151 2023-08-01       53\n",
       "152 2023-09-01       37\n",
       "153 2023-10-01       35\n",
       "154 2023-11-01       24\n",
       "155 2023-12-01       24\n",
       "\n",
       "[156 rows x 2 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('AR_complaints_new.csv')\n",
    "df['datetime'] = pd.to_datetime(df['datetime'])\n",
    "monthly = df['datetime'].groupby(df.datetime.dt.to_period(\"M\")).agg('count')\n",
    "monthly = pd.DataFrame(monthly)\n",
    "monthly.columns= (['monthly'])\n",
    "monthly = monthly.reset_index(drop=False)\n",
    "monthly['datetime'] = monthly['datetime'].dt.to_timestamp('s').dt.strftime('%Y-%m-%d')\n",
    "monthly['datetime'] = pd.to_datetime(monthly['datetime'])\n",
    "#monthly\n",
    "date_range = pd.date_range(start=\"2011-01-01\", end=\"2023-12-31\", freq='MS')\n",
    "date_list = date_range.strftime('%Y-%m-%d').tolist()\n",
    "date_list = pd.DataFrame(date_list)\n",
    "date_list.columns = (['datetime'])\n",
    "date_list['datetime'] = pd.to_datetime(date_list['datetime'])\n",
    "merged_df = pd.merge(date_list, monthly, on='datetime', how='outer')\n",
    "\n",
    "# 将 NaN 填充为 0\n",
    "merged_df = merged_df.fillna(0)\n",
    "#merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "644904fa-fc7a-4fe5-b3f2-dfe1ffb90e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pulaski', 'Benton', 'Washington', 'Sebastian', 'Craighead',\n",
       "       'Faulkner', 'Garland', 'Jefferson', 'Pope', 'Mississippi',\n",
       "       'Saline', 'Union', 'White', 'Independence', 'Crawford'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = pd.read_csv('./AR_gdp_average_250210.csv')\n",
    "top15_list = gdp.sort_values('mean',ascending=False)[:15].County.values\n",
    "top15_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "44a737bf-8d08-4889-9b9a-abca8ce75891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-03-01</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    datetime  monthly\n",
       "0 2011-01-01       21\n",
       "1 2011-02-01       25\n",
       "2 2011-03-01       24\n",
       "3 2011-04-01       28\n",
       "4 2011-05-01       30"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#top15_list = ['Pulaski','Benton','Washington','Sebastian','Craighead','Faulkner','Garland','Jefferson','Mississippi','Pope','Union','Saline','White','Independence','Crittenden']\n",
    "#top15_list = ['Pulaski', 'Faulkner', 'Benton', 'Saline', 'Washington', 'White',\n",
    "#       'Garland', 'Pope', 'Lonoke', 'Crawford', 'Sebastian'] # top 11 based on numbers\n",
    "\n",
    "# top15_list = ['Pulaski', 'Benton', 'Washington', 'Sevier', 'Craighead',\n",
    "#        'Faulkner', 'Jefferson', 'Garland', 'Pope', 'Union', 'Mississippi',\n",
    "#        'White', 'Scott', 'Independence', 'Crittenden', ]\n",
    "      # 'Crawford','Greene', 'Miller', 'Lonoke', 'Baxter'] # top20 based on GDP\n",
    "\n",
    "top15 = []\n",
    "top15 = pd.DataFrame(top15)\n",
    "for name in top15_list:\n",
    "    res = df[df['County'].str.contains(name)]\n",
    "    top15 = pd.concat([top15,res],axis=0)\n",
    "\n",
    "top15_monthly = top15['datetime'].groupby(top15.datetime.dt.to_period(\"M\")).agg('count')\n",
    "top15_monthly = pd.DataFrame(top15_monthly)\n",
    "top15_monthly.columns= (['monthly'])\n",
    "top15_monthly = top15_monthly.reset_index(drop=False)\n",
    "top15_monthly['datetime'] = top15_monthly['datetime'].dt.to_timestamp('s').dt.strftime('%Y-%m-%d')\n",
    "top15_monthly['datetime'] = pd.to_datetime(top15_monthly['datetime'])\n",
    "\n",
    "top15_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "06172235-e3c1-41d7-99c6-29d05064b5ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>monthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-03-01</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-04-01</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-05-01</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      datetime  monthly\n",
       "0   2011-01-01       21\n",
       "1   2011-02-01       25\n",
       "2   2011-03-01       24\n",
       "3   2011-04-01       28\n",
       "4   2011-05-01       30\n",
       "..         ...      ...\n",
       "151 2023-08-01       31\n",
       "152 2023-09-01       22\n",
       "153 2023-10-01       21\n",
       "154 2023-11-01       21\n",
       "155 2023-12-01       18\n",
       "\n",
       "[156 rows x 2 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_top15 = pd.merge(date_list, top15_monthly, on='datetime', how='outer')\n",
    "\n",
    "# 将 NaN 填充为 0\n",
    "merged_top15 = merged_top15.fillna(0)\n",
    "merged_top15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b1cdf7de-3b3d-4cc6-98be-ce63d3dd89be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    sns.set_style(\"ticks\")         # import sns使得横纵坐标的线加浓加粗\n",
    "    sns.set_context(\"talk\")\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "\n",
    "\n",
    "import datetime\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "line1, = plt.step(merged_df['datetime'], merged_df['monthly'],color='black',ms=9,label='Total complaints')\n",
    "line2,  = plt.step(merged_top15['datetime'], merged_top15['monthly'],color='r',ms=6,label='Top 15 counties')\n",
    "\n",
    "plt.ylabel('Monthly complaints',fontproperties=custom_font,fontsize=25,labelpad=12)\n",
    "plt.xticks(ticks, fontsize=25, rotation=0)\n",
    "ax.set_xticklabels(['2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020','2021','2022','2023','2024'],fontproperties=custom_font,fontsize=25)\n",
    "line_legend = ax.legend(handles=[line1, line2], fontsize=20, loc=(0.5,-0.25), edgecolor='black', title=\"\", ncol=2,columnspacing=1.35)\n",
    "\n",
    "plt.grid(color='grey', linestyle=':', linewidth=0.9, which='major',axis='y')\n",
    "ax.set_xlim([datetime.date(2010, 11, 1), datetime.date(2024, 2, 1)])\n",
    "plt.yticks(np.arange(0,100,20),fontsize=28,color='black')\n",
    "#plt.legend(fontsize=20,loc='upper left',edgecolor='black')\n",
    "#plt.axvline(x=[datetime.datetime(2020, 1, 1)], ymin=0.0, ymax=80, color='black',ls='--', linewidth = 3)\n",
    "#plt.axvline(x=[datetime.datetime(2022, 1, 1)], ymin=0.0, ymax=80, color='black',ls='--',linewidth = 3)\n",
    "\n",
    "#plt.axvline(x = datetime.datetime('2011-2-1'), color = 'b',)\n",
    "\n",
    "flood = plt.axvspan(datetime.datetime(2011,2,1), datetime.datetime(2011,8,1),  color='lightblue',ls='-',alpha=0.3,linewidth = 3,label='Flood')\n",
    "drought = plt.axvspan(datetime.datetime(2012,4,15), datetime.datetime(2013,6,1), color='r',ls='-',alpha=0.1,linewidth = 1,label='Drought')\n",
    "covid = plt.axvspan(datetime.datetime(2020,1,1), datetime.datetime(2022,1,1), color='lightgrey',ls='-',alpha=0.4,linewidth = 3,label='COVID-19')\n",
    "highlight_legend = ax.legend(handles=[flood, drought, covid], fontsize=20, loc=(0.05,-0.25), ncol=3,edgecolor='black', title=\"\",columnspacing=1.35)\n",
    "line_legend.get_frame().set_linewidth(2.5)  # Increase legend box edge width\n",
    "highlight_legend.get_frame().set_linewidth(2.5)  # Increase legend box edge width\n",
    "ax.add_artist(line_legend)\n",
    "\n",
    "ax = plt.gca()\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_linewidth(3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecfeaed2-364b-4716-a45d-6d07afde8f20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "285ba0eb-a891-4838-9a9a-d087d7297006",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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